During a typical imaging workflow process, pages are scanned, morphologically enhanced, assigned textual metadata, and converted to a customer specific deliverable file format and given to the client. At any time during the imaging workflow process, images can be modified on purpose or inadvertently by the scan hardware or people working with the images using a variety of software packages. For architectural and engineering drawings it is critical that the scale of the drawings are maintained in the capture process and discrepancies must be detected. If not detected, the discrepancies in the images, which may include for example scale accuracy, could cost tens to hundreds of thousands of dollars to correct. For example, if an original image is a blue print, the processed image could be scaled too small or too large, resulting in the builder building a wall in the wrong place. Moreover, the business unit performing the image processing could be held accountable due to inaccuracies of the image.
Previous methods of detecting discrepancies in images relied on a manual process. The previous methods trusted the accuracy of scan hardware and training of operators to ensure that inaccuracies would not take place. Thus, it is desirable to provide an automated quality assurance process that automatically detects discrepancies between an original image and a processed image to ensure consistent quality and reduce costs by reducing manual labor inspection of images.